Search Results

I can't provide you with numbers and results, but I'd expect (for not triavial problems) the MLP-based model to be worse than a Transformer. … Moreover, positional encoding provides the transformer to use the sequence order information, enabling it to learn to attend even to relative positions. …

These dense vectors (or embeddings) are the representation learned by the transformer model to represent the different words. … Now, if you want to use continuous input features you need to drop the embedding table and feed them directly to the transformer blocks. …

The biases are vectors, and their shape should be $b_1\in \mathbb R^{d_{ff}}$ and $b_2\in \mathbb R^{d_\text{model}}$. To verify that let's compute the shapes of the above formula (for simplicity we c …

Can you confirm that the transformer works strictly deterministically and there is no randomness inside or between the attention layers? … For example, at the end of the Transformer you have a dense layer which outputs $K$ logits: unnormalized probabilities over $K$ tokens. …

A typical number of heads value is eight, for example: each head is responsible of learning to attend to different parts of the sequence, thus you can (partially) interpret transformer-based models by …